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Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks
2018 IEEE International Congress on Big Data (BigData Congress), 2018Short-term traffic prediction allows Intelligent Transport Systems to proactively respond to events before they happen. With the rapid increase in the amount, quality, and detail of traffic data, new techniques are required that can exploit the information in the data in order to provide better results while being able to scale and cope with increasing
Zainab Abbas +3 more
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A New Delay Connection for Long Short-Term Memory Networks
International Journal of Neural Systems, 2018Connections play a crucial role in neural network (NN) learning because they determine how information flows in NNs. Suitable connection mechanisms may extensively enlarge the learning capability and reduce the negative effect of gradient problems. In this paper, a new delay connection is proposed for Long Short-Term Memory (LSTM) unit to develop a ...
Jianyong Wang 0002 +3 more
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Conditioning and time representation in long short-term memory networks
Biological Cybernetics, 2013Dopaminergic models based on the temporal-difference learning algorithm usually do not differentiate trace from delay conditioning. Instead, they use a fixed temporal representation of elapsed time since conditioned stimulus onset. Recently, a new model was proposed in which timing is learned within a long short-term memory (LSTM) artificial neural ...
François Rivest +2 more
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Matching Biomedical Ontologies with Long Short-Term Memory Networks
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020Semantic Web (SW) has attracted the increasing attention of researchers, which facilitates people to link and handle various data. Ontology is the kernel technique of SW, and biomedical ontology is a state-of-art biomedical knowledge modeling technique, which formally defines the biomedical concepts and their relationships. However, the same biomedical
Chao Jiang, Xingsi Xue
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A Long-Short Term Memory Network for Detecting CRISPR Arrays
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019Clustered Regularly Interspaced Short Palindromic Repeat is a pattern found in the DNA sequences of some archeal and bacterial organisms. Together with CRISPR associated genes, CRISPR arrays provide immunity against phages and other mobile exogenous elements. CRISPR-based immunity mechanism can be manipulated to perform genome editing at low cost.
Shantanu Deshmukh +2 more
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Optical Music Recognition by Long Short-Term Memory Networks
2018Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score ...
Arnau Baró +3 more
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CCG supertagging with bidirectional long short-term memory networks
Natural Language Engineering, 2017AbstractNeural Network-based approaches have recently produced good performances in Natural language tasks, such as Supertagging. In the supertagging task, a Supertag (Lexical category) is assigned to each word in an input sequence. Combinatory Categorial Grammar Supertagging is a more challenging problem than various sequence-tagging problems, such as
Rekia Kadari +3 more
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Dialogue Intent Classification with Long Short-Term Memory Networks
2018Dialogue intent analysis plays an important role for dialogue systems. In this paper, we present a deep hierarchical LSTM model to classify the intent of a dialogue utterance. The model is able to recognize and classify user’s dialogue intent in an efficient way.
Lian Meng, Minlie Huang
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Forecasting container throughput with long short-term memory networks
Industrial Management & Data Systems, 2019PurposeBetter forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other ...
Sonali Shankar +3 more
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Deep long short-term memory networks for speech recognition
2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP), 2016Speech recognition has been significantly improved by applying acoustic models based on deep neural network which could be realized as the feedforward NN (FNN) or the recurrent NN (RNN). In general, FNN is feasible to project the observations onto a deep invariant feature space while RNN is beneficial to capture the temporal information in a sequential
Jen-Tzung Chien, Alim Misbullah
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